Computer implemented visual object classification, also called object recognition, pertain to the classifying of visual representations of real-life objects found in still images or motion videos captured by a camera. By performing visual object classification, each visual object found in the still images or motion video is classified according to its type (e.g. human, vehicle, animal).
Automated security and surveillance systems typically employ video cameras or other image capturing devices or sensors to collect image data. In the simplest systems, images represented by the image data are displayed for contemporaneous screening by security personnel and/or recorded for later reference after a security breach. In those systems, the task of detecting and classifying visual objects of interest is performed by a human observer. A significant advance occurs when the system itself is able to perform object detection and classification, either partly or completely.
In a typical surveillance system, for example, one may be interested in detecting objects such as humans, vehicles, animals, etc. that move through the environment. Different objects might pose different threats or levels of alarm. For example, an animal in the scene may be normal, but a human or vehicle in the scene may be cause for an alarm and may require the immediate attention of a security guard. Automated computer-implemented detection and classification of objects in the images represented by the image data captured by the cameras can significantly facilitate the task of screening of the security personnel as well as improving recording of image data.